{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-03-05T12:51:33.277951Z",
     "start_time": "2025-03-05T12:51:33.264914Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "\n",
    "us_file_path = './youtube_video_data/US_video_data_numbers.csv'\n",
    "uk_file_path = './youtube_video_data/UK_video_data_numbers.csv'\n",
    "\n",
    "t1 = np.loadtxt(us_file_path,delimiter = ',',dtype = 'int',unpack = True)\n",
    "t2 = np.loadtxt(us_file_path,delimiter = ',',dtype = 'int')\n",
    "\n",
    "# print(t1)\n",
    "# print(t2)\n",
    "print('*' * 100)\n",
    "print(t2)\n",
    "print('*' * 100)\n",
    "\n",
    "#取行\n",
    "print(t2[2])\n",
    "print('*' * 100)\n",
    "#取连续的多行\n",
    "print(t2[2:])\n",
    "print('*' * 100)\n",
    "#取不连续的多行\n",
    "print(t2[[2,8,10]])\n",
    "print('*' * 100)\n",
    "#取连续的多列\n",
    "print(t2[:,2:])\n",
    "print('*' *50)\n",
    "#取不连续的多列\n",
    "print(t2[:,[0,2]])\n",
    "print('*' * 50)\n",
    "#取多行和多列\n",
    "b = t2[2:5,1:4]\n",
    "print(b)\n",
    "print('*' * 50)\n",
    "#取多个不相邻的点(  (0,0)(2,1)(2,3)\n",
    "c = t2[[0,2,2],[0,1,3]] \n",
    "print(c)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************************************************************************\n",
      "[[4394029  320053    5931   46245]\n",
      " [7860119  185853   26679       0]\n",
      " [5845909  576597   39774  170708]\n",
      " ...\n",
      " [ 142463    4231     148     279]\n",
      " [2162240   41032    1384    4737]\n",
      " [ 515000   34727     195    4722]]\n",
      "****************************************************************************************************\n",
      "[5845909  576597   39774  170708]\n",
      "****************************************************************************************************\n",
      "[[5845909  576597   39774  170708]\n",
      " [2642103   24975    4542   12829]\n",
      " [1168130   96666     568    6666]\n",
      " ...\n",
      " [ 142463    4231     148     279]\n",
      " [2162240   41032    1384    4737]\n",
      " [ 515000   34727     195    4722]]\n",
      "****************************************************************************************************\n",
      "[[5845909  576597   39774  170708]\n",
      " [1338533   69687     678    5643]\n",
      " [ 859289   34485     726    1914]]\n",
      "****************************************************************************************************\n",
      "[[  5931  46245]\n",
      " [ 26679      0]\n",
      " [ 39774 170708]\n",
      " ...\n",
      " [   148    279]\n",
      " [  1384   4737]\n",
      " [   195   4722]]\n",
      "**************************************************\n",
      "[[4394029    5931]\n",
      " [7860119   26679]\n",
      " [5845909   39774]\n",
      " ...\n",
      " [ 142463     148]\n",
      " [2162240    1384]\n",
      " [ 515000     195]]\n",
      "**************************************************\n",
      "[[576597  39774 170708]\n",
      " [ 24975   4542  12829]\n",
      " [ 96666    568   6666]]\n",
      "**************************************************\n",
      "[4394029  576597  170708]\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 通过matplotlib绘制直方图",
   "id": "aee3f8fdbae7ef0a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-05T12:59:55.853658Z",
     "start_time": "2025-03-05T12:59:55.629544Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "us_file_path = './youtube_video_data/US_video_data_numbers.csv'\n",
    "uk_file_path = './youtube_video_data/UK_video_data_numbers.csv'\n",
    "\n",
    "t_us = np.loadtxt(us_file_path,delimiter = ',',dtype = 'int')\n",
    "\n",
    "#取评论的数据\n",
    "t_us_comments = t_us[:,-1] #取最后一列\n",
    "\n",
    "# print(t_us)\n",
    "# print('*' * 50)\n",
    "# print(t_us_comments)\n",
    "\n",
    "#怎么知道分多少，取最大值最小值\n",
    "print(t_us_comments.max(),t_us_comments.min())\n",
    "\n",
    "d = 10000\n",
    "\n",
    "bin_nums = (t_us_comments.max() - t_us_comments.min()) // d\n",
    "\n",
    "#绘图\n",
    "plt.figure(figsize = (20,10),dpi = 90)\n",
    "\n",
    "plt.hist(t_us_comments,bins = bin_nums)\n",
    "\n",
    "plt.show()"
   ],
   "id": "34dba4ef0f9ec83f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "582624 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x900 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-05T13:02:00.761967Z",
     "start_time": "2025-03-05T13:02:00.585489Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "us_file_path = './youtube_video_data/US_video_data_numbers.csv'\n",
    "uk_file_path = './youtube_video_data/GB_video_data_numbers.csv'\n",
    "\n",
    "t_us = np.loadtxt(us_file_path,delimiter = ',',dtype = 'int')\n",
    "\n",
    "#取评论的数据\n",
    "t_us_comments = t_us[:,-1] #取最后一列\n",
    "\n",
    "# print(t_us)\n",
    "# print('*' * 50)\n",
    "# print(t_us_comments)\n",
    "\n",
    "#选择比5000小的数据\n",
    "t_us_comments = t_us_comments[t_us_comments <= 5000]\n",
    "\n",
    "#怎么知道分多少，取最大值最小值\n",
    "print(t_us_comments.max(),t_us_comments.min())\n",
    "\n",
    "d = 50\n",
    "\n",
    "bin_nums = (t_us_comments.max() - t_us_comments.min()) // d\n",
    "\n",
    "#绘图\n",
    "plt.figure(figsize = (20,10),dpi = 90)\n",
    "\n",
    "plt.hist(t_us_comments,bins = bin_nums)\n",
    "\n",
    "plt.show()"
   ],
   "id": "126d692885662564",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4995 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x900 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 英国评论数和喜欢数的关系（散点图",
   "id": "fc0da43af0f3f830"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-05T13:08:00.084157Z",
     "start_time": "2025-03-05T13:07:59.957375Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "us_file_path = './youtube_video_data/US_video_data_numbers.csv'\n",
    "uk_file_path = './youtube_video_data/GB_video_data_numbers.csv'\n",
    "\n",
    "t_uk = np.loadtxt(uk_file_path,delimiter = ',',dtype = 'int')\n",
    "\n",
    "t_uk_comment = t_uk[:,-1]\n",
    "t_uk_like = t_uk[:,1]\n",
    "\n",
    "plt.figure(figsize = (20,10),dpi = 90)\n",
    "plt.scatter(t_uk_like,t_uk_comment)\n",
    "\n",
    "plt.show()"
   ],
   "id": "91be1ba82c25c2c4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x900 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-06T02:09:22.907435Z",
     "start_time": "2025-03-06T02:09:22.045878Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "us_file_path = './youtube_video_data/US_video_data_numbers.csv'\n",
    "uk_file_path = './youtube_video_data/GB_video_data_numbers.csv'\n",
    "\n",
    "t_uk = np.loadtxt(uk_file_path,delimiter = ',',dtype = 'int')\n",
    "\n",
    "#选择喜欢数小于50万的数据\n",
    "t_uk = t_uk[t_uk[:,1]<=500000]\n",
    "t_uk_comment = t_uk[:,-1]\n",
    "t_uk_like = t_uk[:,1]\n",
    "\n",
    "plt.figure(figsize = (20,10),dpi = 90)\n",
    "plt.scatter(t_uk_like,t_uk_comment)\n",
    "\n",
    "plt.show()"
   ],
   "id": "c934d687327c0128",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x900 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "c5d3ac2b66c7c981"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
